Intensifying a heuristic forest harvest scheduling search procedure with 2-opt decision choices

1999 ◽  
Vol 29 (11) ◽  
pp. 1784-1792 ◽  
Author(s):  
Pete Bettinger ◽  
Kevin Boston ◽  
John Sessions
Keyword(s):  
Heuristic Search ◽  
Planning Horizon ◽  
Efficient Solutions ◽  
Search Process ◽  
Harvest Scheduling ◽  
Time Period ◽  
Harvest Timing ◽  
Management Problems ◽  
Decision Choices ◽  
Feasible Solutions

Forest management problems with even-flow and adjacency considerations are difficult to solve optimally. A heuristic search intensification process, which uses two types of decision procedures, changes to single-decision choices (1-opt moves) and changes to two-decision choices simultaneously (2-opt moves), was used in an attempt to locate feasible and efficient solutions to these problems. One-opt moves involve changing the timing of timber harvests for a single land unit and are commonly used in heuristic techniques. Two-opt moves involve swapping the harvest timing between two land units, which intensify the search process. We apply the procedures to two management problems, one with 40 land units and the other with 700 land units. The goal is to achieve the highest, and most even, flow of timber volume over five time periods, with adjacent units being unavailable for harvest in the same period. One-opt moves, used alone, allowed the search process to produce good feasible solutions to these management problems and to generate a relatively even spread (number) of harvests over the planning horizon. The use of 2-opt moves resulted in better solutions, although the number of harvests per time period remained static. These procedures, used alone, may not be appropriate for all problems, because of their nature and limitations.

Effect of decision variable definition and data aggregation on a search process applied to a single-tree simulator

2001 ◽  
Vol 31 (6) ◽  
pp. 1057-1066 ◽  
Author(s):  
Peder Wikström
Keyword(s):  
Tabu Search ◽  
Data Aggregation ◽  
Net Present Value ◽  
Planning Horizon ◽  
Solution Technique ◽  
Search Process ◽  
Decision Variable ◽  
Stand Management ◽  
Harvest Timing ◽  
Execution Times

This paper focuses on how computer execution times and net present value (NPV) are affected by different groupings of tree-selection harvest controls, different procedures to determine harvest timing, and tree data aggregation. The problems related to stand management are viewed as a hierarchy, where the main problem is determining harvest periods and the subordinate problem is determining what trees to cut in a given set of harvest periods. The solution technique is a derivative-free search process, and the objective is to maximize the NPV of harvest revenues for a stand over a given planning horizon. The tree-selection harvest controls are based on diameter and species groupings. The procedure to determine harvest timing is based on Tabu search and fixed cutting cycles, respectively. Sensitivity analysis is performed for a selection of stands in southern Sweden, where each stand is represented by a set of inventoried plots. Both even-aged and uneven-aged management are considered. Solutions improved with the number of decision variables. The Tabu search procedure proved very efficient at determining harvest periods for the even-aged problems. For the uneven-aged problems, fixed cutting cycles approximated the harvest timing problem, but at considerably shorter execution times. It is suggested that aggregated data be used for determining harvest timing, after which, using the original nonaggregated data, the tree-selection problem for a given set of harvest periods can be resolved.

A METHOD FOR GENERATING ALL THE EFFICIENT SOLUTIONS OF A 0-1 MULTI-OBJECTIVE LINEAR PROGRAMMING PROBLEM

2004 ◽  
Vol 21 (01) ◽  
pp. 127-139 ◽  
Author(s):  
G. R. JAHANSHAHLOO ◽  
F. HOSSEINZADEH LOTFI ◽  
N. SHOJA ◽  
G. TOHIDI
Keyword(s):  
Linear Programming ◽  
Programming Problem ◽  
Linear Programming Problem ◽  
Feasible Solution ◽  
Efficient Solutions ◽  
Optimal Solutions ◽  
Objective Functions ◽  
Multi Objective ◽  
Feasible Solutions ◽  
Single Objective

In this paper, a method using the concept of l1-norm is proposed to find all the efficient solutions of a 0-1 Multi-Objective Linear Programming (MOLP) problem. These solutions are specified without generating all feasible solutions. Corresponding to a feasible solution of a 0-1 MOLP problem, a vector is constructed, the components of which are the values of objective functions. The method consists of a one-stage algorithm. In each iteration of this algorithm a 0-1 single objective linear programming problem is solved. We have proved that optimal solutions of this 0-1 single objective linear programming problem are efficient solutions of the 0-1 MOLP problem. Corresponding to efficient solutions which are obtained in an iteration, some constraints are added to the 0-1 single objective linear programming problem of the next iteration. Using a theorem we guarantee that the proposed algorithm generates all the efficient solutions of the 0-1 MOLP problem. Numerical results are presented for an example taken from the literature to illustrate the proposed algorithm.

scholarly journals A Generalized MILP Formulation for the Period-Aggregated Resource Leveling Problem with Variable Job Duration

Algorithms ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 6 ◽  
Author(s):  
Ilia Tarasov ◽  
Alain Haït ◽  
Olga Battaïa
Keyword(s):  
Optimization Problem ◽  
Mathematical Formulation ◽  
Planning Horizon ◽  
Resource Leveling ◽  
Job Duration ◽  
Time Period ◽  
Decision Variables ◽  
Additional Costs ◽  
Flexible Resource ◽  
The Given

We study a resource leveling problem with variable job duration. The considered problem includes both scheduling and resource management decisions. The planning horizon is fixed and separated into a set of time periods of equal length. There are several types of resources and their amount varies from one period to another. There is a set of jobs. For each job, a fixed volume of work has to be completed without any preemption while using different resources. If necessary, extra resources can be used at additional costs during each time period. The optimization goal is to minimize the total overload costs required for the execution of all jobs by the given deadline. The decision variables specify the starting time of each job, the duration of the job and the resource amount assigned to the job during each period (it may vary over periods). We propose a new generalized mathematical formulation for this optimization problem. The formulation is compared with existing approaches from the literature. Theoretical study and computational experiments show that our approach provides more flexible resource allocation resulting in better final solutions.

scholarly journals An Empirical Evaluation of Spatial Restrictions in Industrial Harvest Scheduling: The SFI Planning Problem

2001 ◽  
Vol 25 (2) ◽  
pp. 60-68 ◽  
Author(s):  
Karl R. Walters ◽  
Eric S. Cox
Keyword(s):  
Empirical Evaluation ◽  
Block Size ◽  
Planning Horizon ◽  
Planning Problem ◽  
Harvest Scheduling ◽  
Planning Software ◽  
Spatial Parameters ◽  
Harvest Volume ◽  
The Cost ◽  
The Impact

Abstract Member companies of the American Forest and Paper Association have adopted common operating principles called the Sustainable Forestry InitiativeSM (SFI) that call for marked reductions in the size of clearcut harvest areas, greenup intervals and proximity restrictions on the harvest of adjacent areas. A commercially available hierarchical planning software suite is used to analyze the impact of the adjacency and harvest size objectives within SFI on a representative forest of the U.S. Southeast. Ten alternative, spatially feasible tactical schedules were developed for a 15 yr planning horizon and achieved 73.4 to 75.6% of the harvest volume predicted by the nonspatial strategic harvest schedule. Spatially feasible harvest levels were negatively affected by increasingly restrictive spatial parameters: the cost of increasing the greenup interval by 1 yr was at least 5% of the strategic harvest volume, and reducing the maximum allowable block size from 180 ac to 60 ac yielded a 10% reduction in harvest. The planning software has been implemented at Champion International Corporation, providing planners with a valuable tool for strategic and tactical forest planning. South. J. Appl. For. 25(2):60–68.

Medication Management Problems Reported by Subscribers to a School Nurse Listserv

2001 ◽  
Vol 17 (3) ◽  
pp. 131-139 ◽  
Author(s):  
Thomas J. Reutzel ◽  
Rinku Patel
Keyword(s):  
Drug Therapy ◽  
Management Practices ◽  
Medication Management ◽  
Discussion Group ◽  
School Nurse ◽  
School Nurses ◽  
Empirical Inquiry ◽  
Time Period ◽  
Pharmacy Profession ◽  
Management Problems

Given the potentially serious consequences of suboptimum medication management practices in elementary and secondary schools and the fact that this topic has been subject to little empirical inquiry, the purpose of this study was to obtain a preliminary understanding of the types of medication management problems that school nurses face, as well as the strategies they use to solve those problems. An analysis of messages related to medication management that appeared on the SCHLRN-L listserv (a 1,400-member discussion group for schools nurses) was performed. All messages sent to the listserv during a 5-month period were monitored. Any discussion thread that began during this time period and addressed the management of medications was selected for possible inclusion in the study. The result was a group of 71 threads. The listserv participants described a wide array of medication management problems and suggested numerous strategies for solving them. This study shows that serious medication management problems exist in schools and that school nurses make use of various strategies or “tricks of the trade” to handle these problems. This research highlights the need for a contribution from the pharmacy profession in this important and neglected area of drug therapy.

Peanut Yield Decline in the Southeast and Economically Feasible Solutions

1993 ◽  
Vol 20 (1) ◽  
pp. 36-40 ◽  
Author(s):  
M. C. Lamb ◽  
J. I. Davidson ◽  
C. L. Butts
Keyword(s):  
Agricultural Policy ◽  
Field Data ◽  
System Technology ◽  
Policy Changes ◽  
Yield Decline ◽  
Systems Model ◽  
Time Period ◽  
Arachis Hypogaea L ◽  
Time Periods ◽  
Feasible Solutions

Abstract Peanut (Arachis hypogaea L.) yields in the Southeast declined 368 kg/ha during the 1980's and 596 kg/ha in the late 1980's when compared to the 1974-1979 period. The peanut yield decline was correlated with significant meteorological and policy changes in the Southeast during each time period. Meteorological changes were statistically estimated to decrease peanut yield by 137 kg/ha and 194 kg/ha in the 1980's and late 1980's, respectively. The effect on peanut yields from changes in agricultural policy and management decisions was estimated at -166 kg/ha and -225 kg/ha during the respective time periods. Field data gathered during crop years 1987-1990 reflected the meteorological and policy changes during the late 1980's in both non-irrigated and irrigated peanut fields. EXNUT, an expert systems model for peanut irrigation and pest management, was evaluated in 1987, 1988, 1989, and 1990 against expert farmers in the Southeast and increased peanut yields by 204 kg/ha. Evaluation of EXNUT demonstrated one example of how expert system technology can offer economically feasible solutions to the peanut yield decline in the Southeast.

MASSIVELY PARALLEL IDA* SEARCH

1993 ◽  
Vol 02 (02) ◽  
pp. 163-180 ◽  
Author(s):  
DIANE J. COOK ◽  
GARY LYONS
Keyword(s):  
Artificial Intelligence ◽  
Motion Planning ◽  
Heuristic Search ◽  
Massively Parallel ◽  
Search Process ◽  
Planning Problem ◽  
Robot Arm ◽  
Speed Up ◽  
Arm Motion ◽  
Computational Bottleneck

Heuristic search is a fundamental component of Artificial Intelligence applications. Because search routines are frequently also a computational bottleneck, numerous methods have been explored to increase the efficiency of search. Recently, researchers have begun investigating methods of using parallel MIMD and SIMD hardware to speed up the search process. In this paper, we present a massively-parallel SIMD approach to search named MIDA* search. The components of MIDA* include a very fast distribution algorithm which biases the search to one side of the tree, and an incrementally-deepening depthfirst search of all the processors in parallel. We show the results of applying MIDA* to instances of the Fifteen Puzzle problem and to the robot arm motion planning problem. Results reveal an efficiency of 74% and a speedup of 8553 and 492 over serial and 16-processor MIMD algorithms, respectively, when finding a solution to the Fifteen Puzzle problem that is close to optimal.

scholarly journals Solution procedures for block selection and sequencing in flat-bedded potash underground mines

OR Spectrum ◽  
2021 ◽  
Author(s):  
Cinna Seifi ◽  
Marco Schulze ◽  
Jürgen Zimmermann
Keyword(s):  
Crop Yields ◽  
Decisive Role ◽  
Planning Horizon ◽  
Underground Mines ◽  
Mixed Integer ◽  
Nonlinear Program ◽  
Constructive Heuristic ◽  
Processing Plants ◽  
Problem Instances ◽  
Feasible Solutions

AbstractPhosphates, and especially potash, play an essential role in the increase in crop yields. Potash is mined in Germany in underground mines using a conventional drill-and-blast technique. The most commercially valuable mineral contained in potash is the potassium chloride that is separated from the potash in aboveground processing plants. The processing plants perform economically best if the amount of potassium contained in the output is equal to a specific value, the so-called optimal operating point. Therefore, quality-oriented extraction plays a decisive role in reducing processing costs. In this paper, we mathematically formulate a block selection and sequencing problem with a quality-oriented objective function that aims at an even extraction of potash regarding the potassium content. We, thereby, have to observe some precedence relations, maximum and minimum limits of the output, and a quality tolerance range within a given planning horizon. We model the problem as a mixed-integer nonlinear program which is then linearized. We show that our problem is $${\mathcal {NP}}$$ NP -hard in the strong sense with the result that a MILP-solver cannot find feasible solutions for the most challenging problem instances at hand. Accordingly, we develop a problem-specific constructive heuristic that finds feasible solutions for each of our test instances. A comprehensive experimental performance analysis shows that a sophisticated combination of the proposed heuristic with the mathematical program improves the feasible solutions achieved by the heuristic, on average, by $$92.5\%$$ 92.5 % .

scholarly journals Multiobjective fractional programming problems and the sufficient condition involving Hb – (p, r)-η- invex function

2021 ◽  
Vol 336 ◽  
pp. 08015
Author(s):  
Xiaoyan Gao ◽  
Huan Niu
Keyword(s):  
Convex Functions ◽  
Fractional Programming ◽  
Efficient Solutions ◽  
Sufficient Condition ◽  
Sufficient Optimality Condition ◽  
Multiobjective Fractional Programming ◽  
Invex Functions ◽  
Arcwise Connected ◽  
Feasible Solutions ◽  
Invex Function

On the basis of arcwise connected convex functions and (p, r) −η - invex functions, we established Hb –(p, r) –η- invex functions. Based on the generalized invex assumption of new functions, the solutions of a class of multiobjective fractional programming problems are studied, and the sufficient optimality condition for the feasible solutions of multiobjective fractional programming problems to be efficient solutions are established and proved.

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